Infrared Small Target Detection (IRSTD) faces significant challenges due to low signal-to-noise ratios, complex backgrounds, and the absence of discernible target features. While deep learning-based encoder-decoder frameworks have advanced the field, their static pattern learning suffers from pattern drift across diverse scenarios (\emph{e.g.}, day/night variations, sky/maritime/ground domains), limiting robustness. To address this, we propose IrisNet, a novel meta-learned framework that dynamically adapts detection strategies to the input infrared image status. Our approach establishes a dynamic mapping between infrared image features and entire decoder parameters via an image-to-decoder transformer. More concretely, we represent the parameterized decoder as a structured 2D tensor preserving hierarchical layer correlations and enable the transformer to model inter-layer dependencies through self-attention while generating adaptive decoding patterns via cross-attention. To further enhance the perception ability of infrared images, we integrate high-frequency components to supplement target-position and scene-edge information. Experiments on NUDT-SIRST, NUAA-SIRST, and IRSTD-1K datasets demonstrate the superiority of our IrisNet, achieving state-of-the-art performance.

翻译：红外小目标检测（IRSTD）因信噪比低、背景复杂且缺乏可辨别的目标特征而面临重大挑战。尽管基于深度学习的编码器-解码器框架推动了该领域的发展，但其静态模式学习在不同场景（例如昼夜变化、天空/海洋/地面域）中存在模式漂移问题，限制了鲁棒性。为解决此问题，我们提出了IrisNet，一种新颖的元学习框架，能够根据输入红外图像的状态动态调整检测策略。我们的方法通过图像到解码器的Transformer，建立了红外图像特征与整个解码器参数之间的动态映射。具体而言，我们将参数化解码器表示为保留层级层相关性的结构化二维张量，使Transformer能够通过自注意力建模层间依赖关系，同时通过交叉注意力生成自适应解码模式。为进一步增强对红外图像的感知能力，我们整合高频分量以补充目标位置和场景边缘信息。在NUDT-SIRST、NUAA-SIRST和IRSTD-1K数据集上的实验证明了IrisNet的优越性，实现了最先进的性能。